Charge-Domain In-Memory Computing With NAND-Compatible Ferroelectric Memcapacitor Crossbar.

Rapid growth in artificial intelligence has intensified the need for energy-efficient computing architectures beyond the conventional von Neumann paradigm. While memristive in-memory computing has attracted significant attention, its low cell resistance causes static power consumption, IR-drop-induced errors, and scalability limitations. Here, we demonstrate a NAND-compatible ferroelectric memcapacitor crossbar for scalable and energy-efficient in-memory computing. The proposed architecture employs charge-domain operation with intrinsically high cell resistance, strongly suppressing static conduction and reducing energy consumption. Optimized device design enables a large charge memory window, reliable multilevel storage, and non-destructive readout. Experimental vector-matrix multiplication (VMM) exhibits highly linear behavior due to displacement-current-based computation, which alleviates IR-drop effects. Circuit-level validation confirms practical sensing feasibility, while parasitic-aware SPICE simulations based on a 3D NAND architecture verify robust operation under deeply stacked conditions. Finally, CIFAR-10 image classification and spiking neural network inference highlight the strong potential of ferroelectric memcapacitor arrays as a scalable platform for energy-efficient neuromorphic and in-memory computing applications.